Cloud Services for Big Data Analytics

June 27 2014Second International Workshop on Service and Cloud Based Data

Integration (SCDI 2014)Anchorage AKGeoffrey Fox

gcf@indiana.edu http://www.infomall.org

School of Informatics and ComputingDigital Science Center

Indiana University Bloomington

Abstract• We present a software model built on the Apache software stack

(ABDS) that is well used in modern cloud computing, which we enhance with HPC concepts to derive HPC-ABDS. – We discuss layers in this stack

• We discuss how to implement this in a world of multiple infrastructures and evolving software environments for users, developers and administrators

• We present Cloudmesh as supporting Software-Defined Distributed System as a Service or SDDSaaS with multiple services on multiple clouds/HPC systems.

• We use a sample of over 50 big data applications to identify characteristics of data intensive applications and propose a big data version of the famous Berkeley dwarfs and NAS parallel benchmarks.– We consider hardware from clouds to HPC. – We illustrate issues with examples with image data– This tells you needed services

http://www.kpcb.com/internet-trends

Note largest science ~100 petabytes = 0.000025 total

HPC-ABDS

Integrating High Performance Computing with Apache Big Data Stack

Shantenu Jha, Judy Qiu, Andre Luckow

• HPC-ABDS• ~120 Capabilities• >40 Apache• Green layers have strong HPC Integration opportunities

• Goal• Functionality of ABDS• Performance of HPC

Broad Layers in HPC-ABDS• Workflow-Orchestration• Application and Analytics: Mahout, MLlib, R…• High level Programming• Basic Programming model and runtime

– SPMD, Streaming, MapReduce, MPI• Inter process communication

– Collectives, point-to-point, publish-subscribe• In-memory databases/caches• Object-relational mapping• SQL and NoSQL, File management• Data Transport• Cluster Resource Management (Yarn, Slurm, SGE)• File systems(HDFS, Lustre …)• DevOps (Puppet, Chef …)• IaaS Management from HPC to hypervisors (OpenStack)• Cross Cutting

– Message Protocols– Distributed Coordination– Security & Privacy– Monitoring

Useful Set of Analytics Architectures• Pleasingly Parallel: including local machine learning as in

parallel over images and apply image processing to each image- Hadoop could be used but many other HTC, Many task tools

• Search: including collaborative filtering and motif finding implemented using classic MapReduce (Hadoop)

• Map-Collective or Iterative MapReduce using Collective Communication (clustering) – Hadoop with Harp, Spark …..

• Map-Communication or Iterative Giraph: (MapReduce) with point-to-point communication (most graph algorithms such as maximum clique, connected component, finding diameter, community detection)– Vary in difficulty of finding partitioning (classic parallel load balancing)

• Shared memory: thread-based (event driven) graph algorithms (shortest path, Betweenness centrality)Ideas like workflow are “orthogonal” to this

Getting High Performance on Data Analytics (e.g. Mahout, R…)

• On the systems side, we have two principles:– The Apache Big Data Stack with ~120 projects has important broad

functionality with a vital large support organization– HPC including MPI has striking success in delivering high performance, however with a fragile sustainability model

• There are key systems abstractions which are levels in HPC-ABDS software stack where Apache approach needs careful integration with HPC– Resource management– Storage– Programming model -- horizontal scaling parallelism– Collective and Point-to-Point communication– Support of iteration– Data interface (not just key-value)

• In application areas, we define application abstractions to support:– Graphs/network – Geospatial– Genes– Images, etc.

HPC-ABDS HourglassHPC ABDSSystem (Middleware)

High performanceApplications

• HPC Yarn for Resource management• Horizontally scalable parallel programming model• Collective and Point-to-Point communication• Support of iteration (in memory databases)

System Abstractions/standards• Data format• Storage

120 Software Projects

Application Abstractions/standardsGraphs, Networks, Images, Geospatial ….

SPIDAL (Scalable Parallel Interoperable Data Analytics Library) or High performance Mahout, R, Matlab…

Parallel Global Machine Learning Examples

Mahout and Hadoop MR – Slow due to MapReducePython slow as ScriptingSpark Iterative MapReduce, non optimal communicationHarp Hadoop plug in with ~MPI collectives MPI fastest as C not Java

Increasing Communication Identical Computation

Clustering and MDS Large Scale O(N2) GML

WDA SMACOF MDS (Multidimensional Scaling) using Harp on Big Red 2 Parallel Efficiency: on 100-300K sequences

Conjugate Gradient (dominant time) and Matrix Multiplication

0 20 40 60 80 100 120 1400.00

0.20

0.40

0.60

0.80

1.00

1.20

100K points 200K points 300K points

Number of Nodes

Para

llel E

ffici

ency

Features of Harp Hadoop Plugin• Hadoop Plugin (on Hadoop 1.2.1 and Hadoop 2.2.0)• Hierarchical data abstraction on arrays, key-values and

graphs for easy programming expressiveness.• Collective communication model to support various

communication operations on the data abstractions• Caching with buffer management for memory allocation

required from computation and communication • BSP style parallelism• Fault tolerance with checkpointing

Building a Big Data Ecosystem that is broadly deployable

Using Lots of Services• To enable Big data processing, we need to support those processing data,

those developing new tools and those managing big data infrastructure• Need Software, CPU’s, Storage, Networks delivered as Software-Defined

Distributed System as a Service or SDDSaaS– SDDSaaS integrates component services from lower levels of Kaleidoscope up to

different Mahout or R components and the workflow services that integrate them

• Given richness and rapid evolution of field, we need to enable easy use of the Kaleidoscope (and other) software.

• Make a list of basic software services needed• Then define them as Puppet/Chef Puppies/recipes• Compose them with SDDSL Language (later)• Specify infrastructures• Administrators, developers run Cloudmesh to deploy on demand• Application users directly access Data Analytics as Software as a Service

created by Cloudmesh

Infrastructure

IaaS Software Defined

Computing (virtual Clusters) Hypervisor, Bare Metal Operating System

Platform

PaaS Cloud e.g. MapReduce HPC e.g. PETSc, SAGA Computer Science e.g.

Compiler tools, Sensor nets, Monitors

Software-Defined Distributed System (SDDS) as a Service

NetworkNaaS

Software Defined Networks

OpenFlow GENI

Software(ApplicationOr Usage)

SaaS

CS Research Use e.g. test new compiler or storage model

Class Usages e.g. run GPU & multicore

Applications

FutureGrid usesSDDS-aaS Tools

Provisioning Image Management IaaS Interoperability NaaS, IaaS tools Expt management Dynamic IaaS NaaS DevOps

CloudMesh is a SDDSaaS tool that uses Dynamic Provisioning and Image Management to provide custom environments for general target systemsInvolves (1) creating, (2) deploying, and (3) provisioning of one or more images in a set of machines on demand http://cloudmesh.futuregrid.org/18

Maybe a Big Data Initiative would include

• OpenStack• Slurm• Yarn• Hbase• MySQL• iRods• Memcached• Kafka• Harp

• Hadoop, Giraph, Spark• Storm• Hive• Pig• Mahout – lots of different

analytics• R -– lots of different analytics• Kepler, Pegasus, Airavata• Zookeeper• Ganglia, Nagios, Inca

CloudMesh Architecture• Cloudmesh is a SDDSaaS toolkit to support

– A software-defined distributed system encompassing virtualized and bare-metal infrastructure, networks, application, systems and platform software with a unifying goal of providing Computing as a Service.

– The creation of a tightly integrated mesh of services targeting multiple IaaS frameworks

– The ability to federate a number of resources from academia and industry. This includes existing FutureGrid infrastructure, Amazon Web Services, Azure, HP Cloud, Karlsruhe using several IaaS frameworks

– The creation of an environment in which it becomes easier to experiment with platforms and software services while assisting with their deployment.

– The exposure of information to guide the efficient utilization of resources. (Monitoring)

– Support reproducible computing environments– IPython-based workflow as an interoperable onramp

• Cloudmesh exposes both hypervisor-based and bare-metal provisioning to users and administrators

• Access through command line, API, and Web interfaces.

Cloudmesh Architecture• Cloudmesh

Management Framework for monitoring and operations, user and project management, experiment planning and deployment of services needed by an experiment

• Provisioning and execution environments to be deployed on resources to (or interfaced with) enable experiment management.

• Resources.FutureGrid, SDSC Comet, IU Juliet

Cloudmesh Functionality

Building Blocks of Cloudmesh• Uses internally Libcloud and Cobbler• Celery Task/Query manager (AMQP - RabbitMQ)• MongoDB

• Accesses via abstractions external systems/standards• OpenPBS, Chef• Openstack (including tools like Heat), AWS EC2, Eucalyptus,

Azure• Xsede user management (Amie) via Futuregrid• Implementing Slurm, OCCI, Ansible, Puppet

• Evaluating Razor, Juju, Xcat (Original Rain used this), Foreman

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Cloudmesh User Interface

25

26

Cloudmesh Shell & bash & IPython

SDDS Software Defined Distributed Systems• Cloudmesh builds infrastructure as SDDS consisting of one or more virtual clusters or slices

with extensive built-in monitoring• These slices are instantiated on infrastructures with various owners• Controlled by roles/rules of Project, User, infrastructure

Python or REST API

User in Project

CMPlan

CMProv

CMMon

Infrastructure (Cluster, Storage,

Network, CPS) Instance Type Current State Management

Structure Provisioning

Rules Usage Rules

(depends on user roles)

Results

CMExecUser Roles

User role and infrastructure rule dependent security

checks

Request Execution in Project 

Request SDDS 

SelectPlan 

Requested SDDS as federated Virtual

Infrastructures #1Virtual

infra.Linux #2 Virtual

infra.Windows #3Virtual

infra.Linux #4 Virtual

infra.Mac OS X

Repository

Image and Template

Library

SDDSL

One needs general hypervisor and bare-metal slices to support FG research

The experiment management system is intended to integrates ISI Precip, FG Cloudmesh and tools latter invokes

Enables reproducibility in experiments.

What is SDDSL?• There is an OASIS standard activity TOSCA (Topology

and Orchestration Specification for Cloud Applications)• But this is similar to mash-ups or workflow (Taverna,

Kepler, Pegasus, Swift ..) and we know that workflow itself is very successful but workflow standards are not– OASIS WS-BPEL (Business Process Execution Language)

didn’t catch on• As basic tools (Cloudmesh) use Python and Python is a

popular scripting language for workflow, we suggest that Python is SDDSL– IPython Notebooks are natural log of execution provenance

Cloudmesh as an On-Ramp• As an On-Ramp, CloudMesh deploys recipes on

multiple platforms so you can test in one place and do production on others

• Its multi-host support implies it is effective at distributed systems

• It will support traditional workflow functions such as– Specification of an execution dataflow – Customization of Recipe– Specification of program parameters

• Workflow quite well explored in Python https://wiki.openstack.org/wiki/NovaOrchestration/WorkflowEngines

• IPython notebook preserves provenance of activity

CloudMesh Administrative View of SDDS aaS• CM-BMPaaS (Bare Metal Provisioning aaS) is a systems view and allows

Cloudmesh to dynamically generate anything and assign it as permitted by user role and resource policy– FutureGrid machines India, Bravo, Delta, Sierra, Foxtrot are like this– Note this only implies user level bare metal access if given user is authorized and this

is done on a per machine basis– It does imply dynamic retargeting of nodes to typically safe modes of operation

(approved machine images) such as switching back and forth between OpenStack, OpenNebula, HPC on Bare metal, Hadoop etc.

• CM-HPaaS (Hypervisor based Provisioning aaS) allows Cloudmesh to generate "anything" on the hypervisor allowed for a particular user– Platform determined by images available to user– Amazon, Azure, HPCloud, Google Compute Engine

• CM-PaaS (Platform as a Service) makes available an essentially fixed Platform with configuration differences– XSEDE with MPI HPC nodes could be like this as is Google App Engine and Amazon

HPC Cluster. Echo at IU (ScaleMP) is like this– In such a case a system administrator can statically change base system but the

dynamic provisioner cannot

CloudMesh User View of SDDS aaS• Note we always consider virtual clusters or slices with nodes

that may or may not have hypervisors• BM-IaaS: Bare Metal (root access) Infrastructure as a service

with variants e.g. can change firmware or not• H-IaaS: Hypervisor based Infrastructure (Machine) as a

Service. User provided a collection of hypervisors to build system on.– Classic Commercial cloud view

• PSaaS Physical or Platformed System as a Service where user provided a configured image on either Bare Metal or a Hypervisor– User could request a deployment of Apache Storm and Kafka to

control a set of devices (e.g. smartphones)

Cloudmesh Infrastructure Types• Nucleus Infrastructure:

– Persistent Cloudmesh Infrastructure with defined provisioning rules and characteristics and managed by CloudMesh

• Federated Infrastructure:– Outside infrastructure that can be used by special arrangement such as

commercial clouds or XSEDE– Typically persistent and often batch scheduled– CloudMesh can use within prescribed provisioning rules and users restricted

to those with permitted access; interoperable templates allow common images to nucleus

• Contributed Infrastructure– Outside contributions to a particular Cloudmesh project managed by

Cloudmesh in this project– Typically strong user role restrictions – users must belong to a particular

project– Can implement a Planetlab like environment by contributing hardware that

can be generally used with bare-metal provisioning

NIST Big Data Use Cases

Chaitin Baru, Bob Marcus, Wo Chang co-leaders

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Use Case Template• 26 fields completed for 51

areas• Government Operation: 4• Commercial: 8• Defense: 3• Healthcare and Life Sciences:

10• Deep Learning and Social

Media: 6• The Ecosystem for Research:

4• Astronomy and Physics: 5• Earth, Environmental and

Polar Science: 10• Energy: 1

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51 Detailed Use Cases: Contributed July-September 2013Covers goals, data features such as 3 V’s, software, hardware

• http://bigdatawg.nist.gov/usecases.php• https://bigdatacoursespring2014.appspot.com/course (Section 5)• Government Operation(4): National Archives and Records Administration, Census Bureau• Commercial(8): Finance in Cloud, Cloud Backup, Mendeley (Citations), Netflix, Web Search,

Digital Materials, Cargo shipping (as in UPS)• Defense(3): Sensors, Image surveillance, Situation Assessment• Healthcare and Life Sciences(10): Medical records, Graph and Probabilistic analysis,

Pathology, Bioimaging, Genomics, Epidemiology, People Activity models, Biodiversity• Deep Learning and Social Media(6): Driving Car, Geolocate images/cameras, Twitter, Crowd

Sourcing, Network Science, NIST benchmark datasets• The Ecosystem for Research(4): Metadata, Collaboration, Language Translation, Light source

experiments• Astronomy and Physics(5): Sky Surveys including comparison to simulation, Large Hadron

Collider at CERN, Belle Accelerator II in Japan• Earth, Environmental and Polar Science(10): Radar Scattering in Atmosphere, Earthquake,

Ocean, Earth Observation, Ice sheet Radar scattering, Earth radar mapping, Climate simulation datasets, Atmospheric turbulence identification, Subsurface Biogeochemistry (microbes to watersheds), AmeriFlux and FLUXNET gas sensors

• Energy(1): Smart grid

26 Features for each use case Biased to science

Big Data Patterns – the OgresWhat services are needed?

Would like to capture “essence of these use cases”

“small” kernels, mini-appsOr Classify applications into patterns

Do it from HPC background not database viewpointe.g. focus on cases with detailed analytics

Section 5 of my class https://bigdatacoursespring2014.appspot.com/preview classifies 51 use

cases with ogre facets

What are “mini-Applications”• Use for benchmarks of computers and software (is my parallel

compiler any good?)• In parallel computing, this is well established

– Linpack for measuring performance to rank machines in Top500 (changing?)

– NAS Parallel Benchmarks (originally a pencil and paper specification to allow optimal implementations; then MPI library)

– Other specialized Benchmark sets keep changing and used to guide procurements

• Last 2 NSF hardware solicitations had NO preset benchmarks – perhaps as no agreement on key applications for clouds and data intensive applications

– Berkeley dwarfs capture different structures that any approach to parallel computing must address

– Templates used to capture parallel computing patterns• Also database benchmarks like TPC

HPC Benchmark Classics• Linpack or HPL: Parallel LU factorization for solution of

linear equations• NPB version 1: Mainly classic HPC solver kernels

– MG: Multigrid– CG: Conjugate Gradient– FT: Fast Fourier Transform– IS: Integer sort– EP: Embarrassingly Parallel– BT: Block Tridiagonal– SP: Scalar Pentadiagonal– LU: Lower-Upper symmetric Gauss Seidel

13 Berkeley Dwarfs• Dense Linear Algebra • Sparse Linear Algebra• Spectral Methods• N-Body Methods• Structured Grids• Unstructured Grids• MapReduce• Combinational Logic• Graph Traversal• Dynamic Programming• Backtrack and Branch-and-Bound• Graphical Models• Finite State Machines

First 6 of these correspond to Colella’s original. Monte Carlo dropped.N-body methods are a subset of Particle in Colella.

Note a little inconsistent in that MapReduce is a programming model and spectral method is a numerical method.Need multiple facets!

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51 Use Cases: What is Parallelism Over?• People: either the users (but see below) or subjects of application and often both• Decision makers like researchers or doctors (users of application)• Items such as Images, EMR, Sequences below; observations or contents of online

store– Images or “Electronic Information nuggets”– EMR: Electronic Medical Records (often similar to people parallelism)– Protein or Gene Sequences;– Material properties, Manufactured Object specifications, etc., in custom dataset– Modelled entities like vehicles and people

• Sensors – Internet of Things• Events such as detected anomalies in telescope or credit card data or atmosphere• (Complex) Nodes in RDF Graph• Simple nodes as in a learning network• Tweets, Blogs, Documents, Web Pages, etc.

– And characters/words in them• Files or data to be backed up, moved or assigned metadata• Particles/cells/mesh points as in parallel simulations

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51 Use Cases: Low-Level (Run-time) Computational Types

• PP(26): Pleasingly Parallel or Map Only• MR(18 +7 MRStat): Classic MapReduce• MRStat(7): Simple version of MR where key computations

are simple reduction as coming in statistical averages• MRIter(23): Iterative MapReduce• Graph(9): complex graph data structure needed in analysis • Fusion(11): Integrate diverse data to aid

discovery/decision making; could involve sophisticated algorithms or could just be a portal

• Streaming(41): some data comes in incrementally and is processed this way (Count) out of 51

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51 Use Cases: Higher-Level Computational Types or Features

• Classification(30): divide data into categories• S/Q/Index(12): Search and Query• CF(4): Collaborative Filtering• Local ML(36): Local Machine Learning • Global ML(23): Deep Learning, Clustering, LDA, PLSI, MDS, Large Scale

Optimizations as in Variational Bayes, Lifted Belief Propagation, Stochastic Gradient Descent, L-BFGS, Levenberg-Marquardt (Sometimes call EGO or Exascale Global Optimization)

• Workflow: (Left out of analysis but very common)• GIS(16): Geotagged data and often displayed in ESRI, Microsoft Virtual Earth,

Google Earth, GeoServer etc.• HPC(5): Classic large-scale simulation of cosmos, materials, etc. generates big

data• Agent(2): Simulations of models of data-defined macroscopic entities

represented as agents

Not Independent

Global Machine Learning aka EGO – Exascale Global Optimization

• Typically maximum likelihood or 2 with a sum over the N data items – documents, sequences, items to be sold, images etc. and often links (point-pairs). Usually it’s a sum of positive number as in least squares

• Covering clustering/community detection, mixture models, topic determination, Multidimensional scaling, (Deep) Learning Networks

• PageRank is “just” parallel linear algebra• Note many Mahout algorithms are sequential – partly as

MapReduce limited; partly because parallelism unclear– MLLib (Spark based) better

• SVM and Hidden Markov Models do not use large scale parallelization in practice?

• Detailed papers on particular parallel graph algorithms

One example:Image based Applications

http://www.kpcb.com/internet-trends

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17:Pathology Imaging/ Digital Pathology I• Application: Digital pathology imaging is an emerging field where examination of

high resolution images of tissue specimens enables novel and more effective ways for disease diagnosis. Pathology image analysis segments massive (millions per image) spatial objects such as nuclei and blood vessels, represented with their boundaries, along with many extracted image features from these objects. The derived information is used for many complex queries and analytics to support biomedical research and clinical diagnosis.

HealthcareLife Sciences

MR, MRIter, PP, Classification Parallelism over ImagesStreaming

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17:Pathology Imaging/ Digital Pathology II

• Current Approach: 1GB raw image data + 1.5GB analytical results per 2D image. MPI for image analysis; MapReduce + Hive with spatial extension on supercomputers and clouds. GPU’s used effectively. Figure below shows the architecture of Hadoop-GIS, a spatial data warehousing system over MapReduce to support spatial analytics for analytical pathology imaging.

HealthcareLife Sciences

• Futures: Recently, 3D pathology imaging is made possible through 3D laser technologies or serially sectioning hundreds of tissue sections onto slides and scanning them into digital images. Segmenting 3D microanatomic objects from registered serial images could produce tens of millions of 3D objects from a single image. This provides a deep “map” of human tissues for next generation diagnosis. 1TB raw image data + 1TB analytical results per 3D image and 1PB data per moderated hospital per year.

Architecture of Hadoop-GIS, a spatial data warehousing system over MapReduce to support spatial analytics for analytical pathology imaging

Parallelism over images or over pixels within image (especially for GPU)

18: Computational Bioimaging

• Application: Data delivered from bioimaging is increasingly automated, higher resolution, and multi-modal. This has created a data analysis bottleneck that, if resolved, can advance the biosciences discovery through Big Data techniques.

• Current Approach: The current piecemeal analysis approach does not scale to situation where a single scan on emerging machines is 32 TB and medical diagnostic imaging is annually around 70 PB even excluding cardiology. One needs a web-based one-stop-shop for high performance, high throughput image processing for producers and consumers of models built on bio-imaging data.

• Futures: Goal is to solve that bottleneck with extreme scale computing with community-focused science gateways to support the application of massive data analysis toward massive imaging data sets. Workflow components include data acquisition, storage, enhancement, minimizing noise, segmentation of regions of interest, crowd-based selection and extraction of features, and object classification, organization, and search. Use ImageJ, OMERO, VolRover, advanced segmentation and feature detection software.

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HealthcareLife Sciences

Largely Local Machine Learning and Pleasingly Parallel

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26: Large-scale Deep Learning• Application: Large models (e.g., neural networks with more neurons and connections) combined with

large datasets are increasingly the top performers in benchmark tasks for vision, speech, and Natural Language Processing. One needs to train a deep neural network from a large (>>1TB) corpus of data (typically imagery, video, audio, or text). Such training procedures often require customization of the neural network architecture, learning criteria, and dataset pre-processing. In addition to the computational expense demanded by the learning algorithms, the need for rapid prototyping and ease of development is extremely high.

• Current Approach: The largest applications so far are to image recognition and scientific studies of unsupervised learning with 10 million images and up to 11 billion parameters on a 64 GPU HPC Infiniband cluster. Both supervised (using existing classified images) and unsupervised applications

Deep LearningSocial Networking

• Futures: Large datasets of 100TB or more may be necessary in order to exploit the representational power of the larger models. Training a self-driving car could take 100 million images at megapixel resolution. Deep Learning shares many characteristics with the broader field of machine learning. The paramount requirements are high computational throughput for mostly dense linear algebra operations, and extremely high productivity for researcher exploration. One needs integration of high performance libraries with high level (python) prototyping environments

IN

Classified OUT

MRIter,EGO Classification Parallelism over Nodes in NN, Data being classified

Global Machine Learning but Stochastic Gradient Descent only use small fraction of total images (100’s) at each iteration so parallelism over images not clearly useful

27: Organizing large-scale, unstructured collections of consumer photos I

• Application: Produce 3D reconstructions of scenes using collections of millions to billions of consumer images, where neither the scene structure nor the camera positions are known a priori. Use resulting 3D models to allow efficient browsing of large-scale photo collections by geographic position. Geolocate new images by matching to 3D models. Perform object recognition on each image. 3D reconstruction posed as a robust non-linear least squares optimization problem where observed relations between images are constraints and unknowns are 6-D camera pose of each image and 3D position of each point in the scene.

• Current Approach: Hadoop cluster with 480 cores processing data of initial applications. Note over 500 billion images (too small) on Facebook and over 5 billion on Flickr with over 1800 (was 500 a year ago) million images added to social media sites each day.

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Deep LearningSocial NetworkingGlobal Machine Learning after Initial Local steps

27: Organizing large-scale, unstructured collections of consumer photos II

• Futures: Need many analytics, including feature extraction, feature matching, and large-scale probabilistic inference, which appear in many or most computer vision and image processing problems, including recognition, stereo resolution, and image denoising. Need to visualize large-scale 3D reconstructions, and navigate large-scale collections of images that have been aligned to maps.

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Deep LearningSocial Networking

Global Machine Learning after Initial Local ML pleasingly parallel steps

36: Catalina Real-Time Transient Survey (CRTS): a digital, panoramic, synoptic sky survey I

• Application: The survey explores the variable universe in the visible light regime, on time scales ranging from minutes to years, by searching for variable and transient sources. It discovers a broad variety of astrophysical objects and phenomena, including various types of cosmic explosions (e.g., Supernovae), variable stars, phenomena associated with accretion to massive black holes (active galactic nuclei) and their relativistic jets, high proper motion stars, etc. The data are collected from 3 telescopes (2 in Arizona and 1 in Australia), with additional ones expected in the near future (in Chile).

• Current Approach: The survey generates up to ~ 0.1 TB on a clear night with a total of ~100 TB in current data holdings. The data are preprocessed at the telescope, and transferred to Univ. of Arizona and Caltech, for further analysis, distribution, and archiving. The data are processed in real time, and detected transient events are published electronically through a variety of dissemination mechanisms, with no proprietary withholding period (CRTS has a completely open data policy). Further data analysis includes classification of the detected transient events, additional observations using other telescopes, scientific interpretation, and publishing. In this process, it makes a heavy use of the archival data (several PB’s) from a wide variety of geographically distributed resources connected through the Virtual Observatory (VO) framework.

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Astronomy & Physics

PP, ML, Classification

Parallelism over Images and Events: Celestial events identified in Telescope Images

Streaming, workflow

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36: Catalina Real-Time Transient Survey (CRTS): a digital, panoramic, synoptic sky survey II

• Futures: CRTS is a scientific and methodological testbed and precursor of larger surveys to come, notably the Large Synoptic Survey Telescope (LSST), expected to operate in 2020’s and selected as the highest-priority ground-based instrument in the 2010 Astronomy and Astrophysics Decadal Survey. LSST will gather about 30 TB per night.

Astronomy & Physics

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43: Radar Data Analysis for CReSIS Remote Sensing of Ice Sheets IV

• Typical CReSIS echogram with Detected Boundaries. The upper (green) boundary is between air and ice layer while the lower (red) boundary is between ice and terrain

Earth, Environmental and Polar Science

PP, GIS Parallelism over Radar ImagesStreaming

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44: UAVSAR Data Processing, Data Product Delivery, and Data Services II

• Combined unwrapped coseismic interferograms for flight lines 26501, 26505, and 08508 for the October 2009 – April 2010 time period. End points where slip can be seen on the Imperial, Superstition Hills, and Elmore Ranch faults are noted. GPS stations are marked by dots and are labeled.

Earth, Environmental and Polar Science

PP, GIS Parallelism over Radar ImagesStreaming

Other Facets of the Ogres

Application Class Facet of Ogres• Classification (30) divide data into categories• Search Index and query (12)• Maximum Likelihood or 2 minimizations• Expectation Maximization (often Steepest descent) • Local (pleasingly parallel) Machine Learning (36) contrasted to• (Exascale) Global Optimization (23) (such as Learning Networks,

Variational Bayes and Gibbs Sampling) • Do they Use Agents (2) as in epidemiology (swarm approaches)?

Higher-Level Computational Types or Features in earlier slide also hasCF(4): Collaborative Filtering in Core Analytics Facet and two categories in data source and styleGIS(16): Geotagged data and often displayed in ESRI, Microsoft Virtual Earth, Google Earth, GeoServer etc.HPC(5): Classic large-scale simulation of cosmos, materials, etc. generates big data

Problem Architecture Facet of Ogres (Meta or MacroPattern)i. Pleasingly Parallel – as in BLAST, Protein docking, some

(bio-)imagery including Local Analytics or Machine Learning – ML or filtering pleasingly parallel, as in bio-imagery, radar images (pleasingly parallel but sophisticated local analytics)

ii. Classic MapReduce for Search and Queryiii. Global Analytics or Machine Learning requiring iterative

programming modelsiv. Problem set up as a graph as opposed to vector, gridv. SPMD (Single Program Multiple Data)vi. Bulk Synchronous Processing: well-defined compute-

communication phasesvii. Fusion: Knowledge discovery often involves fusion of multiple

methods. viii. Workflow (often used in fusion)Note problem and machine architectures are related

Slight expansion of an earlier slides on:

Major Analytics Architectures in Use CasesPleasingly parallel (Map-Only)Search (MapReduce)Map-CollectiveMap-Communication as in MPIShared Memory

Low-Level (Run-time) Computational Types used to label 51 use casesPP(26): Pleasingly Parallel MR(18 +7 MRStat): Classic MapReduceMRStat(7)MRIter(23)Graph(9) Fusion(11)Streaming(41) In data source

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4 Forms of MapReduce

 

(a) Map Only (d) Point to Point(c) Iterative Map Reduce or Map-Collective

(b) Classic MapReduce

   

Input

    

map   

      

reduce

 

Input

    

map

   

      reduce

IterationsInput

Output

map

   

Pij

BLAST Analysis

Local Machine Learning

Pleasingly Parallel

High Energy Physics

(HEP) Histograms

Distributed search

 

Classic MPI

PDE Solvers and

particle dynamics

 Domain of MapReduce and Iterative Extensions

MPI

Giraph

Expectation maximization

Clustering e.g. K-means

Linear Algebra, PageRank 

All of them are Map-Communication?

One Facet of Ogres has Computational Featuresa) Flops per byte; b) Communication Interconnect requirements; c) Is application (graph) constant or dynamic?d) Most applications consist of a set of interconnected

entities; is this regular as a set of pixels or is it a complicated irregular graph?

e) Is communication BSP or Asynchronous? In latter case shared memory may be attractive;

f) Are algorithms Iterative or not?g) Data Abstraction: key-value, pixel, graph, vector

Are data points in metric or non-metric spaces? h) Core libraries needed: matrix-matrix/vector algebra,

conjugate gradient, reduction, broadcast

Data Source and Style Facet of Ogres• (i) SQL• (ii) NOSQL based• (iii) Other Enterprise data systems (10 examples from Bob Marcus) • (iv) Set of Files (as managed in iRODS)• (v) Internet of Things• (vi) Streaming and • (vii) HPC simulations• (viii) Involve GIS (Geographical Information Systems)• Before data gets to compute system, there is often an initial data gathering

phase which is characterized by a block size and timing. Block size varies from month (Remote Sensing, Seismic) to day (genomic) to seconds or lower (Real time control, streaming)

• There are storage/compute system styles: Shared, Dedicated, Permanent, Transient

• Other characteristics are needed for permanent auxiliary/comparison datasets and these could be interdisciplinary, implying nontrivial data movement/replication

Analytics Facet (kernels) of the Ogres

Core Analytics Facet of Ogres (microPattern) I• Map-Only• Pleasingly parallel - Local Machine Learning • MapReduce: Search/Query• Summarizing statistics as in LHC Data analysis (histograms)• Recommender Systems (Collaborative Filtering) • Linear Classifiers (Bayes, Random Forests)• Global Analytics• Nonlinear Solvers (structure depends on objective function)

– Stochastic Gradient Descent SGD– (L-)BFGS approximation to Newton’s Method– Levenberg-Marquardt solver

• Map-Collective I (need to improve/extend Mahout, MLlib)• Outlier Detection, Clustering (many methods), • Mixture Models, LDA (Latent Dirichlet Allocation), PLSI (Probabilistic

Latent Semantic Indexing)

Core Analytics Facet of Ogres (microPattern) II• Map-Collective II• Use matrix-matrix,-vector operations, solvers (conjugate gradient)• SVM and Logistic Regression• PageRank, (find leading eigenvector of sparse matrix)• SVD (Singular Value Decomposition)• MDS (Multidimensional Scaling)• Learning Neural Networks (Deep Learning)• Hidden Markov Models• Map-Communication• Graph Structure (Communities, subgraphs/motifs, diameter,

maximal cliques, connected components)• Network Dynamics - Graph simulation Algorithms (epidemiology)• Asynchronous Shared Memory• Graph Structure (Betweenness centrality, shortest path)

Lessons / Insights• Integrate (don’t compete) HPC with “Commodity Big data”

(Google to Amazon to Enterprise Data Analytics) – i.e. improve Mahout; don’t compete with it– Use Hadoop plug-ins rather than replacing Hadoop

• Enhanced Apache Big Data Stack HPC-ABDS has ~120 members • Opportunities at Resource management, Data/File, Streaming,

Programming, monitoring, workflow layers for HPC and ABDS integration

• Need to capture as services – developing a HPC-Cloud interoperability environment

• Data intensive algorithms do not have the well developed high performance libraries familiar from HPC– Need to develop needed services at all levels of stack from users of

Mahout to those developing better run time and programming environments